Using Causal Discovery to Identify Drivers and Controls of Streamflow in Large Sample Hydrology

Catchments are the fundamental units of hydrological analysis and integrate a vast number of physical, biological, and anthropogenic processes. Traditional hydrological modelling approaches, however, adopt a bottom-up perspective, aggregating small-scale physical principles to predict large-scale catchment behaviour. While effective for prediction, this approach can fall short in advancing our understanding of emergent processes and their interactions given the strong dependence on a priori assumptions. To address this gap, causal discovery algorithms offer a promising alternative by moving beyond simple correlation to directly identifying the dynamic causal structures emerging at the catchment scale. In this study, we applied the PCMCI+ algorithm to the CAMELS-US dataset in combination with a subsequent causal effect estimation. We explored how and to what extent dynamic causal structures can be learned from hydro-meteorological data alone, and which catchment properties and conditions influence their expression. We find that causal discovery in hydrology faces challenges due to non-stationarity, unsuitable conditional independence tests, and unmet methodological assumptions. Despite these limitations, our approach reconstructed physically plausible relationships controlled by meaningful catchment properties. These results highlight the potential of causal discovery in hydrology, where it could serve as a complementary framework for model evaluation studies or as an integral part of the model development process.